Role of lipidomics in assessing the functional lipid composition in breast milk
Bibliographic record
Abstract
Breast milk is the ideal source of nutrients for infants in early life. Lipids represent 2-5% of the total breast milk composition and are a major energy source providing 50% of an infant's energy intake. Functional lipids are an emerging class of lipids in breast milk mediating several different biological functions, health, and developmental outcome. Lipidomics is an emerging field that studies the structure and function of lipidome. It provides the ability to identify new signaling molecules, mechanisms underlying physiological activities, and possible biomarkers for early diagnosis and prognosis of diseases, thus laying the foundation for individualized, targeted, and precise nutritional management strategies. This emerging technique can be useful to study the major role of functional lipids in breast milk in several dimensions. Functional lipids are consumed with daily food intake; however, they have physiological benefits reported to reduce the risk of disease. Functional lipids are a new area of interest in lipidomics, but very little is known of the functional lipidome in human breast milk. In this review, we focus on the role of lipidomics in assessing functional lipid composition in breast milk and how lipid bioinformatics, a newly emerging branch in this field, can help to determine the mechanisms by which breast milk affects newborn health.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".